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usage: iCount [-h] [-v] ...
iCount: protein-RNA interaction analysis
========================================
iCount is a Python module and associated command-line interface (CLI), which provides all the
commands needed to process protein-RNA `iCLIP`_ interaction data and to identify and quantify
sites of protein-RNA interactions on RNA.
iCount's main input are FASTQ files with `iCLIP`_ sequencing data, its main output are BED files
with identified and quantified cross-linked sites.
A number of analyses are included in iCount that provide insights into the properties of
protein-RNA interaction.
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
Commands:
releases Get list of available releases.
species Get list of available species.
annotation Download annotation for given release/species/source.
genome Download genome for given release/species/source.
segment Parse annotation file into internal iCount structure - segmentation.
demultiplex Split FASTQ file into separate files, one for each sample barcode.
cutadapt Remove adapter sequences from reads in FASTQ file.
indexstar Generate STAR genome index.
mapstar Map reads to genome with STAR.
xlsites Quantity cross-link events and determine their positions.
annotate Annotate each cross link site with types of regions that intersect with it.
clusters Merge adjacent peaks into clusters and sum cross-links within clusters.
group Merge multiple BED files with crosslinks into one.
peaks Find positions with high density of cross-linked sites.
rnamaps Distribution of cross-links relative to genomic landmarks.
summary Report proportion of cross-link events/sites on each region type.
examples Provide a set of example bash scripts.
man Print help for all commands.
args Print arguments form all CLI commands.
releases
========
usage: iCount releases [-h] [--source] [--species] [-S] [-F] [-P] [-M]
Get list of available releases.
optional arguments:
-h, --help show this help message and exit
--source Source of data. Only ENSEMBL or GENCODE are available (default: gencode)
--species Species name. Only relevant if source is GENCODE (default: None)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
species
=======
usage: iCount species [-h] [--source] [-r] [-S] [-F] [-P] [-M]
Get list of available species.
optional arguments:
-h, --help show this help message and exit
--source Source of data. Only ENSEMBL or GENCODE are available (default: gencode)
-r , --release Release number. Only relevant if source is ENSEMBL (default: None)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
annotation
==========
usage: iCount annotation [-h] [-od] [-a] [--source] [-S] [-F] [-P] [-M]
species release
Download annotation for given release/species/source.
positional arguments:
species Species name
release Release number
optional arguments:
-h, --help show this help message and exit
-od , --out_dir Download to this directory (if not given, current working directory) (default: None)
-a , --annotation Annotation filename (must have .gz file extension). If not given,
species.release.gtf.gz is used. If annotation is provided as absolute
path, value of out_dir parameter is ignored and file is saved to given
absolute path (default: None)
--source Source of data. Only ENSEMBL or GENCODE are available (default: gencode)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
genome
======
usage: iCount genome [-h] [-od] [--genome] [--chromosomes [...]] [--source]
[-S] [-F] [-P] [-M]
species release
Download genome for given release/species/source.
positional arguments:
species Species name
release Release number
optional arguments:
-h, --help show this help message and exit
-od , --out_dir Download to this directory (if not given, current working directory) (default: None)
--genome Genome filename (must have .gz file extension). If not given,
species.release.fa.gz is used. If genome is provided as absolute path,
value of out_dir parameter is ignored and file is saved to given
absolute path (default: None)
--chromosomes [ ...]
If given, do not download the whole genome, but listed
chromosomes only. Only relevant if source is ENSEMBL (default: None)
--source Source of data. Only ENSEMBL or GENCODE are available (default: gencode)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
segment
=======
usage: iCount segment [-h] [-prog] [-S] [-F] [-P] [-M]
annotation segmentation fai
Parse annotation file into internal iCount structure - segmentation.
Currently, only annotations from ENSEMBl and GENCODE are supported.
http://www.gencodegenes.org/
http://www.ensembl.org
Segmentation is used in almost all further analyses.
In segmentation, each transcript is partitioned into so called
regions/intervals. Such regions must span the whole transcript, but should not
intersect with each other. However, higher hierarchy levels: transcripts and
genes can of course intersect each other.
Example of possible segmentation::
Genome level: |---------------------------------------------------|
Gene level: |--------------gene1--------------| |-intergenic-|
|---------gene2--------|
Transcript l.: |----------transcript1---------|
|-------transcript2-------|
|------transcript3-----|
Region level: |-CDS-||-intron-||-CDS-||-UTR3-|
For simplicity, only the partition of transcript1 is presented.
positional arguments:
annotation Path to input GTF file
segmentation Path to output GTF file
fai Path to input genome_file (.fai or similar)
optional arguments:
-h, --help show this help message and exit
-prog, --report_progress
Switch to show progress (default: False)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
demultiplex
===========
usage: iCount demultiplex [-h] [-mis] [-ml] [--prefix] [-od] [-S] [-F] [-P]
[-M]
reads adapter barcodes [barcodes ...]
Split FASTQ file into separate files, one for each sample barcode.
Saved FASTQ files contain sequences where sample barcode, random
barcode, and adapter sequences are removed. Random barcode is moved into
the header line, since it is needed in later steps (removing PCR duplicates
and counting number of cross-link events).
.. autofunction:: iCount.demultiplex.run
positional arguments:
reads Path to reads from a sequencing library
adapter Adapter sequence to remove from ends of reads
barcodes List of barcodes used for library
optional arguments:
-h, --help show this help message and exit
-mis , --mismatches Number of tolerated mismatches when comparing barcodes (default: 1)
-ml , --minimum_length
Minimum length of trimmed sequence to keep (default: 15)
--prefix Prefix of generated FASTQ files (default: demux)
-od , --out_dir Output folder. Use local folder if none given (default: .)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
cutadapt
========
usage: iCount cutadapt [-h] [--qual_trim] [-ml] [-S] [-F] [-P] [-M]
reads reads_trimmed adapter
Remove adapter sequences from reads in FASTQ file.
positional arguments:
reads Input FASTQ file
reads_trimmed Output FASTQ file containing trimmed reads
adapter Sequence of an adapter ligated to the 3' end
optional arguments:
-h, --help show this help message and exit
--qual_trim Trim low-quality bases before adapter removal (default: None)
-ml , --minimum_length
Discard trimmed reads that are shorter than `minimum_length` (default: None)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
indexstar
=========
usage: iCount indexstar [-h] [-a] [--overhang] [--overhang_min] [--threads]
[--genome_sasparsed] [--genome_saindexnbases] [-S]
[-F] [-P] [-M]
genome genome_index
Generate STAR genome index.
positional arguments:
genome Genome sequence to index
genome_index Output folder, where to store genome index
optional arguments:
-h, --help show this help message and exit
-a , --annotation Annotation that defines splice junctions (default: )
--overhang Sequence length around annotated junctions to be used by STAR when
constructing splice junction database (default: 100)
--overhang_min Minimum overhang for unannotated junctions (default: 8)
--threads Number of threads that STAR can use for generating index (default: 1)
--genome_sasparsed STAR parameter genomeSAsparseD.
Suffix array sparsity. Bigger numbers decrease RAM requirements
at the cost of mapping speed reduction. Suggested values
are 1 (30 GB RAM) or 2 (16 GB RAM) (default: 1)
--genome_saindexnbases
STAR parameter genomeSAindexNbases.
SA pre-indexing string length, typically between 10 and 15.
Longer strings require more memory, but result in faster searches (default: 14)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
mapstar
=======
usage: iCount mapstar [-h] [-a] [--multimax] [-mis] [--threads] [-S] [-F] [-P]
[-M]
reads genome_index out_dir
Map reads to genome with STAR.
positional arguments:
reads Sequencing reads to map to genome
genome_index Folder with genome index
out_dir Output folder, where to store mapping results
optional arguments:
-h, --help show this help message and exit
-a , --annotation GTF annotation needed for mapping to splice-junctions (default: )
--multimax Number of allowed multiple hits (default: 10)
-mis , --mismatches Number of allowed mismatches (default: 2)
--threads Number of threads that STAR can use for generating index (default: 1)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
xlsites
=======
usage: iCount xlsites [-h] [-g] [--quant] [--segmentation] [-mis] [--mapq_th]
[--multimax] [--gap_th] [--ratio_th] [-prog] [-S] [-F]
[-P] [-M]
bam sites_unique sites_multi skipped
Quantity cross-link events and determine their positions.
The simplest version of this script would oprate on such example::
|--a---b--- reference sequence, chr 14, positive strand ------------
|rbc1---R1--------|
|rbc1---R2------|
|rbc2---R3------|
|rbc1--------R4-------------|
|rbc3------R5-----|
Five reads (R1-R5) are mapped to a reference sequence (chromosome 14, positive
strand). Reads start on two distinct positons. On first position, there is
R1-R3. Cross-link site is located one nucleotide before start of the read (on
negative strand, one nucleotide after end of read). However, we wish to count
number of cDNA molecules, not the number of reads. This can be done by counting
the number of distinct random barcodes (sometimes also called randomers). So in
upper example, we have:
Postion a: 3 reads, 2 distinct random barcodes = 2 cDNA's
Postion b: 2 reads, 2 distinct random barcodes = 2 cDNA's
However, things can get complicated when a single read is mapped in multiple
parts. This can happen for several reasons. One common example is that introns
are removed during transcription. This can be illustrated with the following
image::
|---------------- reference -----------------------
|--------------------------transcript--------------------------|
|---UTR5---||---intron---||---exon---||---intron---||---exon---|
|-------R1-------|
|--R2.1--> <-R2.2-|
|-R3.1-> <-R3.2-> <-R3.3-|
|-R4.1-> <-R4.2-|
Read R1 and R2 are starting on same position. For the sake of argument, let's
also pretend they also have same random barcode. In so, we would count them as a
single cDNA molecule (= single cross-link event), even though it is obvious that
they represent two separate cross-link events. In order to fix this, we count
not just the number of different randomers on same position, but also number of
different "second-start" coordinates. Second-start coordinate is just the
coordinate of the second part of the read. This way, the actual number of
cross-link events can be determined more accurately. If read is not split, it's
second-start coordinate is 0. If read has multiple "holes" (as read R3) we
determine second-start from the largest hole.
Reads whose second-start do NOT fall on segmentation (like R4) are stored in a
separate BAM file ``sites_strange``. They should be treated with special care,
since they can indicate not-yet annotated features in genome. If segmentation is
not given, all reads with holes, bigger than ``holesize_th`` are considered
strange.
Another parameter needs more explanation: ``group_by``. When algorithm starts,
reads from BAM file are grouped in hierarchical structure by::
* chromosome and strand
* cross-link position
* random barcode
* second-start
Each second-start group receives 1 cDNA score. This score is divided to each
read in group (if there are 5 reads in group, each one gets 1/5 score). This
enables that each read has it's cDNA score and of course, 1 "read score". This
scores can be assigned to start (actually, to cross-link position), midlle or
end position of read. By default, score is of course assigned to cross-link
location. But for diagnostic purpuses, scores can also be assigned to middle or
end coordinate of the read.
TODO: check overlap between unique and multimap BED files, should be small,
otherwise, we should think of a more approapriate quantification of (division
of randomers among) unique mapped sites that overlap with multimapped reads
positional arguments:
bam Input BAM file with mapped reads
sites_unique Output BED6 file to store data from uniquely mapped reads
sites_multi Output BED6 file to store data from multi-mapped reads
skipped Output BAM file to store reads that do not map as expected by segmentation and
reference genome sequence. If read's second start does not fall on any of
segmentation borders, it is considered problematic. If segmentation is not provided,
every read in two parts with gap longer than gap_th is not used (skipped).
All such reads are reported to the user for further exploration
optional arguments:
-h, --help show this help message and exit
-g , --group_by Assign score of a read to either 'start', 'middle' or 'end' nucleotide (default: start)
--quant Report number of 'cDNA' or number of 'reads' (default: cDNA)
--segmentation File with custon segmentation format (obtained by ``iCount segment``) (default: None)
-mis , --mismatches Reads on same position with random barcode differing less than
``mismatches`` are merged together, if their ratio is below ratio_th (default: 1)
--mapq_th Ignore hits with MAPQ < mapq_th (default: 0)
--multimax Ignore reads, mapped to more than ``multimax`` places (default: 50)
--gap_th Reads with gaps less than gap_th are treated as if they have no gap (default: 4)
--ratio_th Ratio between the number of reads supporting a randomer versus the
number of reads supporting the most frequent randomer. All randomers
above this threshold are accepted as unique. Remaining are merged
with the rest, allowing for the specified number of mismatches (default: 0.1)
-prog, --report_progress
Switch to report progress (default: False)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
annotate
========
usage: iCount annotate [-h] [--subtype] [-e [...]] [-S] [-F] [-P] [-M]
annotation sites sites_annotated
Annotate each cross link site with types of regions that intersect with it.
positional arguments:
annotation Path to annotation file (should be GTF and include `subtype` attribute)
sites Path to input BED6 file listing all cross-linked sites
sites_annotated Path to output BED6 file listing annotated cross-linked sites
optional arguments:
-h, --help show this help message and exit
--subtype Subtype (default: biotype)
-e [ ...], --excluded_types [ ...]
Excluded types (default: None)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
clusters
========
usage: iCount clusters [-h] [--dist] [--slop] [-S] [-F] [-P] [-M]
sites peaks clusters
Merge adjacent peaks into clusters and sum cross-links within clusters.
positional arguments:
sites Path to input BED6 file with sites
peaks Path to input BED6 file with peaks (or clusters)
clusters Path to output BED6 file with merged peaks (clusters)
optional arguments:
-h, --help show this help message and exit
--dist Distance between two peaks to merge into same cluster (default: 20)
--slop Distance between site and cluster to assign site to cluster (default: 3)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
group
=====
usage: iCount group [-h] [-S] [-F] [-P] [-M] sites_grouped sites [sites ...]
Merge multiple BED files with crosslinks into one.
First, concatenate files into one file. Then, merge crosslinks from different
files that are on same position and sum their scores.
positional arguments:
sites_grouped Path to output BED6 file containing merged data from input sites files
sites List of BED6 files(paths) to be merged
optional arguments:
-h, --help show this help message and exit
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
peaks
=====
usage: iCount peaks [-h] [--scores] [--features [...]] [-g]
[--merge_features] [--half_window] [--fdr] [-p] [-rnd]
[-prog] [-S] [-F] [-P] [-M]
annotation sites peaks
Find positions with high density of cross-linked sites.
There are two typical variants of this analysis, depending on the parameters:
* Gene-wise analysis, where:
* features = gene
* group_by = gene_id
* Transcript-wise analysis where:
* features = CDS, intron, UTR3, UTR5, ncRNA, intergenic
* group_by = transcript_id
Let's look at the Gene-wise analysis in more detail first. Imagine the following
situation::
|-----------gene1----------|
|-----------------------------gene2------------------------------|
ab c d e
a = 60
b = 100
c = 70
d = 40
e = 100
gene1: gene_id = 001
gene2: gene_id = 002
There are two genes (partially intersecting) and five positions with cross-links
(noted with a, b, c, d and e). Crosslink position "a" has 60 cross-link events,
"b" has 100 cross-link events and so on. Also, gene1 has gene_id 001, etc.
The algorithm first finds all intersections between annotation and
cross-links. In this case cross-link position "a" intersects only with gene1,
while position "b" intersects also with gene2... Annotation can include
various other types of segments (transcripts, intergenic, ncRNA, etc.), but only
segments of type ``gene`` are considered for intersection. This behaviour is
controlled by parameter ``features``.
Next step is to make groups of cross-links. They are grouped by ``group_by``
parameter (in this case, it equals to ``gene_id``). There will be 2 groups.
First group name will be 001 and will contain a, b, c and d. Second group name
will be 002 and will contain b, c, d and e.
The question now is: has any of positions in each group significantly increased
number of cross-link events? And how can one quantify this significance?
This is done by parmutation analysis. It draws a number of random situations
with same group size and number of cross-link scores. Number of such draws is
determined by ``perm`` parameter. This way, a random distribution is calculated.
When comparing the observed distribution with the random one, FDR values are
assigned to each position. A cutoff FRD value is chosen and only positions with
FDR < FDR cutoff are considered as significant.
One must also know that when considering only scores on single positions
significant *clusters* of cross-links can be missed. In the upper example, it is
obvious, that something more significantly is happening on position b than on
position e, despite having the same score. To account for this, algorithm
considers not only the score of single cross-link, but also scores of
cross-links some nucleotides before and after. This behaviour in controlled by
half-window (half_window) parameter. In the upper example, score of position b
eqals to 160 if half_window = 1 and 2530 if half_window=2. Score of position e
remains 100.
Let's also look at the transcript-wise analysis. In this case, scenario also
includes transcripts and sub-transcript elements::
|-----------gene1----------|
|--------transcript1-------|
|-ncRNA-||-intron-||-ncRNA-|
|-----------------------------gene2------------------------------|
|---------------transcript2--------------|
|-CDS-||-intron-||-CDS-||-intron-||-UTR3-|
|---------------transcript3--------------|
|-UTR5-||-intron-||-CDS-||-intron-||-CDS-|
ab c d e
a = 60
b = 100
c = 70
d = 40
e = 100
gene1: gene_id = 001
gene2: gene_id = 002
transcript1: transcript_id = 0001
transcript2: transcript_id = 0002
transcript3: transcript_id = 0003
Value of parameter features is: CDS, intron, UTR3, UTR5, ncRNA, intergenic.
Value of parameter group_by is transcript_id. Since we have multiple values in
feature parameter, another parameter becomes important: merge_features. If set
to false (default) algorithm will make the following groups:
* group name: ncRNA-0001, members: a, b, d
* group name: intron-0001, members: c
* group name: CDS-0002, members: b, c, d
* group name: UTR3-0002, members: e
* group name: intron-0003, members: e
However, if merge_features equals to true, groups are:
* group name: 0001, members: a, b, c, d
* group name: 0002, members: b, c, d, e
* group name: 0003, members: e
Then, for each group, procedure is exactly the same as in Gene-wise case.
When analysis is done, significant positions are reported in file, given by
peaks parameter. If scores parameter is also given, all positions are
reported in it, no matter the FDR value.
positional arguments:
annotation Annotation file in GTF format, obtained from "iCount segment" command
sites File with cross-links in BED6 format
peaks File name for "peaks" output. File reports positions with significant
number of cross-link events. It should have .bed or .bed.gz extension
optional arguments:
-h, --help show this help message and exit
--scores File name for "scores" output. File reports all cross-link events,
independent from their FDR score It should have .tsv, .csv, .txt or .gz
extension (default: None)
--features [ ...] Features from annotation to consider. If None, ['gene'] is used.
Sometimes, it is advised to use ['gene', 'intergenic'] (default: None)
-g , --group_by Attribute by which cross-link positions are grouped (default: gene_id)
--merge_features Treat all features as one when grouping. Has no effect when only one
feature is given in features parameter (default: False)
--half_window Half-window size (default: 3)
--fdr FDR threshold (default: 0.05)
-p , --perms Number of permutations when calculating random distribution (default: 100)
-rnd , --rnd_seed Seed for random generator (default: 42)
-prog, --report_progress
Report analysis progress (default: False)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
rnamaps
=======
usage: iCount rnamaps [-h] [--implicit_handling] [-mis] [--mapq_th]
[--holesize_th] [-S] [-F] [-P] [-M]
bam segmentation out_file strange cross_transcript
Distribution of cross-links relative to genomic landmarks.
What is an RNA-map?
^^^^^^^^^^^^^^^^^^^
Imagine the following situation (on positive strand)::
|---intron1---||---exon2---||---intron2---||---exon3---|
x|-----R1-----|
Situation is simple: a read perfectly maps to reference genome. First cross-link
(nucletide before the start of read R1) is located 2 nucleotides before
landmark of type 'exon-intron'. By examining all cross-links(reads) that are
located in vicinity of landmarks one can obtain the distribution of
cross-link-to-landmark distances. Such distibution is called an RNA map.
Of course, situation gets complicated. Real-world-situation more likely looks
like this::
|--------transcript1-------|
|-ncRNA-||-intron-||-ncRNA-|
|------------------transcript2-----------------|
|--CDS--||-intron-||--CDS--||-intron-||--UTR3--|
|---------------transcript3--------------|
|-UTR5-||-intron-||-CDS-||-intron-||-CDS-|
x|-----R1-----|
x|R2.1-> <--R2.2-|
x|-R3-|
All sort of situations can arise:
* read can intersect with multiple genes/transcripts/segments
simultaneously, which means that one read is relevant for different types
of RNA maps.
* if whole read is mapped to one segment (as is read R3 on transcript2),
then it is impossible to tell the type of RNA map it belongs to: is it
CDS-intron or intron-CDS?
* read can be mapped in multiple parts - for example when introns are
removed before translation, first half of read will map to ``exon1`` and
second half to ``exon2``.
* same cross-link event can be represented by multiple reads with same
random barcode.
* ...
The algorithm takes care for all of this, as explained below. It outputs a file,
that looks like this::
rna_map_type position all explicit
exon-intron 42 13 14
exon-intron 43 123 19
exon-intron 44 56 16
....
intron-CDS 23474 34 2
intron-CDS 23475 85 65
intron-CDS 23476 92 1
...
Each line consits of four columns: RNA map type, position, all and explicit. RNA
map type defines the landmark type, while position defines relative distance of
cross-links to this landmark. All and explicit are counting number of crosslinks
that are ``positions`` away from landmark of type RNA map type. Cross link is
explicit, if read identifying cross-link is mapped to both parts of RNA-map. In
upper example, R1 is explicit, since it maps to intron *and* exon. Read R3 on
transcript2 is implicit, since one has to decide wheather it's cross-link
belongs to ``CDS-intron`` or ``intron-CDS`` RNA map. The term *all* means
explicit + implicit in this context.
If read is implicit (start and stop within same segment), one can choose two
varinats of the algorithm. One option is that whole score of read is given to
RNA-map of the *closest* landmark. Other option is to *split* score on half to
both neighbouring segments. This behaviour is controlled by parameter
implicit_handling.
There are also two cases when a read can map in a way that is not predicted by
annotation:
* For split reads, second start can fall on nucleotide that is not start of
a new segment. This behaviour is excactly the same as in xlsites analysis.
Such reads are reported in file defined with ``strange`` parameter.
* Reads that map on two different transcripts or even two different genes
are repoted in file with name defined in cross_transcript.
-------------------------------------------------------------------------------
Things that still need to be resolved:
* negative strand inspection:
* for each ss_group, take annotation from reverse strand and check what
is the situation on other side.
* What si again the bio-contxt here - explain in docs...?
* Test: Each cross-link should have the same cDNA count as in xlsites
results. The final check should be: sum all scores - the result should be
the same as for xlsites. Actually, the metrics.cross_transcript should
also be considered:
assert: sum(xlsites scores) == sum(RNAmaps "all" + metrics.cross_transc)
* if read crosses two ore more regions from start to end, only end-type is
considered for RNAmap type. Should we fix this and which region type to
take? Since reads are tpyically much shorted than regions, this is
probably a rare case.
-------------------------------------------------------------------------------
Wishes and ideas(Jernej, Tomaz):
* The whole idea is to get a feeling what is happening around landmarks
* separate pre-mRNA and mRNA [this is partially done in _add_entry]
* Life cyle of RNA: part of DNA in transcribed to RNA in nucleus. Introns
are cut out already between this process! Really???
* pre-mRNA contains introns, mature mRNA has no introns and goes outside
from the nucleus.
* so: RNAmap exon-intron will for sure contain only pre-mRNA, but RNAmap
exon-exon can contain pre-mRNA and mRNA... It would be really nice to
report on percentage of theese two categories.
* This can be done by introducing three categories: pre-mRNa, mRNA and
undecided. This can be determined from results - they contain explicit /
implicit info and RNAmap type:
* Intoduce three categories (colors in visualisation?) in every RNAmap type.
Let's take exon-intron as example:
* whole read in left part - color #1 (negative coord, implicit)
* whole read in right part - color #2 (positive coord, implicit)
* read crossing junction - color #3 (explicit)
* How to handle situation when one region in individual's sequence clearly
differs from reference sequence but it is just some variation?
* Change the reference sequence? This can be complex... Make a helper
tool for this?
* Provide additional data in function - which exceptions /abnormalities
to ignore?
* Gaussian smooting of RNAmaps?
* split read - it can differ also on "first-stop", not juts second-start
* we could have some sort of quality check when observing the variance
of first-stop-s. THe major question is: "Do all reads for certain
xlink event indicate the same behaviour?"
positional arguments:
bam BAM file with alligned reads
segmentation GTF file with segmentation. Should be a file produced by function
`get_segments`
out_file Output file with analysis results
strange File with strange propertieas obtained when processing bam file
cross_transcript File with reads spanning over multiple transcripts or multiple genes
optional arguments:
-h, --help show this help message and exit
--implicit_handling Can be 'closest' or 'split'. In case of implicit read - split score to
both neighbours or give it just to the closest neighbour (default: closest)
-mis , --mismatches Reads on same position with random barcode differing less than
``mismatches`` are grouped together (default: 2)
--mapq_th Ignore hits with MAPQ < mapq_th (default: 0)
--holesize_th Raeads with size of holes less than holesize_th are treted as if they
would have no holes (default: 4)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
summary
=======
usage: iCount summary [-h] [--types_length_file] [--digits] [--subtype]
[-e [...]] [-S] [-F] [-P] [-M]
annotation sites summary fai
Report proportion of cross-link events/sites on each region type.
positional arguments:
annotation Path to annotation GTF file (should include subtype attribute)
sites Path to BED6 file listing cross-linked sites
summary Path to output tab-delimited file with summary statistics
fai Path to file with chromosome lengths
optional arguments:
-h, --help show this help message and exit
--types_length_file Path to file with lengths of each type (default: None)
--digits Number of decimal places in results (default: 8)
--subtype Name of attribute to be used as subtype (default: None)
-e [ ...], --excluded_types [ ...]
Types listed in 3rd column of GTF to be exclude from analysis (default: None)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
examples
========
usage: iCount examples [-h] [-od] [-S] [-F] [-P] [-M]
Provide a set of example bash scripts.
.. autofunction:: iCount.examples.run
optional arguments:
-h, --help show this help message and exit
-od , --out_dir Directory to which example scripts should be copied (default: .)
-S , --stdout_log Threshold value (0-50) for logging to stdout. If 0, logging to stdout if turned OFF.
-F , --file_log Threshold value (0-50) for logging to file. If 0, logging to file if turned OFF.
-P , --file_logpath Path to log file.
-M , --results_file File into which to store Metrics.
args
====
usage: iCount args [-h]
optional arguments:
-h, --help show this help message and exit